Instructions to use vorenthiclabs/vorenthos-1.5-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vorenthiclabs/vorenthos-1.5-thinking with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf vorenthiclabs/vorenthos-1.5-thinking # Run inference directly in the terminal: llama cli -hf vorenthiclabs/vorenthos-1.5-thinking
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf vorenthiclabs/vorenthos-1.5-thinking # Run inference directly in the terminal: llama cli -hf vorenthiclabs/vorenthos-1.5-thinking
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf vorenthiclabs/vorenthos-1.5-thinking # Run inference directly in the terminal: ./llama-cli -hf vorenthiclabs/vorenthos-1.5-thinking
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf vorenthiclabs/vorenthos-1.5-thinking # Run inference directly in the terminal: ./build/bin/llama-cli -hf vorenthiclabs/vorenthos-1.5-thinking
Use Docker
docker model run hf.co/vorenthiclabs/vorenthos-1.5-thinking
- LM Studio
- Jan
- vLLM
How to use vorenthiclabs/vorenthos-1.5-thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vorenthiclabs/vorenthos-1.5-thinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vorenthiclabs/vorenthos-1.5-thinking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vorenthiclabs/vorenthos-1.5-thinking
- Ollama
How to use vorenthiclabs/vorenthos-1.5-thinking with Ollama:
ollama run hf.co/vorenthiclabs/vorenthos-1.5-thinking
- Unsloth Studio
How to use vorenthiclabs/vorenthos-1.5-thinking with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vorenthiclabs/vorenthos-1.5-thinking to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vorenthiclabs/vorenthos-1.5-thinking to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vorenthiclabs/vorenthos-1.5-thinking to start chatting
- Docker Model Runner
How to use vorenthiclabs/vorenthos-1.5-thinking with Docker Model Runner:
docker model run hf.co/vorenthiclabs/vorenthos-1.5-thinking
- Lemonade
How to use vorenthiclabs/vorenthos-1.5-thinking with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vorenthiclabs/vorenthos-1.5-thinking
Run and chat with the model
lemonade run user.vorenthos-1.5-thinking-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
vorenthos-1.5-thinking โ Ollama Export
๐ผ๏ธ Vision-Language Model โ accepts both image and text inputs.
Exported from a local Ollama installation and uploaded to the Hugging Face Hub by vorenthiclabs.
Model Details
| Field | Value |
|---|---|
| Base model | gemma4 |
| Finetuned model | vorenthos-1.5-thinking |
| Tag / variant | latest |
| Model type | Vision-Language (Multimodal) |
| Format | GGUF (llama.cpp-compatible) |
| Total size | 7.16 GB |
| Layers | 5 |
Quick Start
With Ollama (recommended)
ollama pull vorenthos-1.5-thinking
ollama run vorenthos-1.5-thinking
With llama.cpp / llama-cpp-python (GGUF)
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="vorenthiclabs/vorenthos-1.5-thinking",
filename="*.gguf",
)
output = llm("Hello, who are you?", max_tokens=256)
print(output["choices"][0]["text"])
Vision input example (llama-cpp-python)
from llama_cpp import Llama
from llama_cpp.llama_chat_format import MoondreamChatHandler # adjust handler per model
llm = Llama.from_pretrained(
repo_id="vorenthiclabs/vorenthos-1.5-thinking",
filename="*.gguf",
chat_handler=MoondreamChatHandler(clip_model_path="mmproj*.gguf"),
n_ctx=4096,
)
response = llm.create_chat_completion(messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
{"type": "text", "text": "Describe this image."}
]
}])
print(response["choices"][0]["message"]["content"])
With Hugging Face transformers + GGUF support
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("vorenthiclabs/vorenthos-1.5-thinking")
model = AutoModelForCausalLM.from_pretrained("vorenthiclabs/vorenthos-1.5-thinking")
File Structure
| File | Description |
|---|---|
config.json |
Ollama model configuration / metadata |
model-*.gguf |
Quantised weights in GGUF format |
tokenizer.jinja |
Chat template |
params.json |
Generation parameters (temperature, top-p, โฆ) |
system_prompt.txt |
Default system prompt embedded in the model |
License
Please check the original model's license before redistribution.
This upload is provided as-is for research and experimentation.
About vorenthiclabs
Visit us at https://huggingface.co/vorenthiclabs.
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